Supporting Diverse Dynamic Intent-based Policies using Janus

Abhashkumar, Anubhavnidhi; Kang, Joon-Myung; Banerjee, Sujata; Zhang, Ying; Wu, Wenfei

doi:10.1145/3143361.3143380

Cited by 44 publications

(30 citation statements)

References 42 publications

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“…Also, the intent solver was designed in [35] to handle the logical consistency problem of multiple intents. As an effective description method, graph-based policy description and multi-domain intent decomposition were adopted in [36], [37]. In particular, the intent monitor and reroute service (IMR) was used in [38] to extend the ONOS Intent Framework so that it can compile multiple intents simultaneously and deploy routes.…”

Section: Academic Researchmentioning

confidence: 99%

A Survey on Intent-Driven Networks

et al. 2020

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Software defined network and network function visualization enhance the network flexibility and management agility, which increase network fragility and complexity. However, the vast majority of network parameters are manually configured, which makes the configuration failures still inevitable. Future networks should be self-configuring, self-managing, and self-optimizing. Intent-driven network (IDN) is a self-driving network that uses decoupling network control logic and closed-loop orchestration techniques to automate application intents. At present, a unified definition of IDN has not yet been presented, and the research background and current status of IDN are not clear. Considering the emerging applications and research of IDN, in this article, we survey existing technologies, clarify definitions, and summarize features for IDN. Specifically, we discuss the basic architecture and key technologies of IDN. In addition, diversity gains and challenges are analyzed briefly. Finally, some future work is highlighted and wider applications of IDN are provided for further research.

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Section: Academic Researchmentioning

confidence: 99%

A Survey on Intent-Driven Networks

et al. 2020

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“…PGA is implemented as an extension of Pyretic. Janus [18] extends the work of PGA by adding the notion of dynamic policies and incorporating QoS constraints. Janus' implementation is also based on Pyretic.…”

Section: Related Workmentioning

confidence: 99%

P4I/O: Intent-Based Networking with P4

Riftadi

Kuipers

2019

2019 IEEE Conference on Network Softwarization (NetSoft)

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Switches that can be (re)programmed through the network programming language P4 are able to completely change-even while in the field-the way they process packets. While powerful, P4 code is inherently static, as it is written and installed to accommodate a particular network requirement. Writing new P4 code each time new requirements arise may be complex and limits our agility to deal with changes in network traffic and services. In this paper, we present P4I/O, a new approach to dataplane programmability based on the philosophy of Intent-Based Networking. P4I/O provides an intent-driven interface that can be used to install and/or remove P4 programs on the switches when needed and which is easy to use. In particular, to realize P4I/O, we (1) describe an extensible Intent Definition Language (IDL), (2) create a repository of P4 code templates, which are parsed and merged based on the intents, (3) provide a technique to realize the resulting P4 program in a programmable switch, while accommodating intent modifications at any time, and finally (4) implement a proof-of-concept to demonstrate that intent modifications can be done on-the-fly.

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“…For example, a neural network could infer the best routes to comply with SLA requirements of the intent without the need of a pre-populated database. However, the decoupling provided by the intent de nition language allows compilations to other existing network con gurations, including other policy languages, such as Janus [1], PGA [16], and Kinectic [11].…”

Section: Intent Deploymentmentioning

confidence: 99%

“…PGA supports Access Control List (ACL) and servicechaining policies, leveraging a graph structure to resolve con icts. Janus [1] extends PGA to support policies with QoS requirements, mobility, and temporal dynamics. More recently, Cocoon [17] introduces a framework focused on guaranteeing correctness of SDN programs that resembles our approach, but it uses rst-order logic instead of machine learning to convert high-level intents into lower level con gurations.…”

Section: Related Workmentioning

confidence: 99%

“…As a proof-of-concept, we implement a service chain for speci c tra c using SONATA-NFV [15]( §4). However, the decoupling provided by the intent de nition language allows the compilation of the intents to other existing network con gurations-including policy languages, such as Janus [1], PGA [16], and Kinectic [11]-improving the reusability of our proposed solution. In this stage, we also make assertions to verify any con icts between the extracted intent and the network con guration-e.g., an intent asking for more bandwidth than is available on the required path-and warn the operator through the chatbot interface.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Refining Network Intents for Self-Driving Networks

Jacobs

Pfitscher

Ferreira³

et al. 2018

Proceedings of the Afternoon Workshop on Self-Driving Networks

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Recent advances in arti cial intelligence (AI) o er an opportunity for the adoption of self-driving networks. However, network operators or home-network users still do not have the right tools to exploit these new advancements in AI, since they have to rely on low-level languages to specify network policies. Intent-based networking (IBN) allows operators to specify high-level policies that dictate how the network should behave without worrying how they are translated into con guration commands in the network devices. However, the existing research proposals for IBN fail to exploit the knowledge and feedback from the network operator to validate or improve the translation of intents. In this paper, we introduce a novel intent-re nement process that uses machine learning and feedback from the operator to translate the operator's u erances into network con gurations. Our re nement process uses a sequence-to-sequence learning model to extract intents from natural language and the feedback from the operator to improve learning. e key insight of our process is an intermediate representation that resembles natural language that is suitable to collect feedback from the operator but is structured enough to facilitate precise translations. Our prototype interacts with a network operator using natural language and translates the operator input to the intermediate representation before translating to SDN rules. Our experimental results show that our process achieves a correlation coe cient squared (i.e., R-squared) of 0.99 for a dataset with 5000 entries and the operator feedback signi cantly improves the accuracy of our model.

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Supporting Diverse Dynamic Intent-based Policies using Janus

Cited by 44 publications

References 42 publications

A Survey on Intent-Driven Networks

A Survey on Intent-Driven Networks

P4I/O: Intent-Based Networking with P4

Refining Network Intents for Self-Driving Networks

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